Ishwari Jaiswal*
Ruturaj Kulkarni
Garima Singh
Vaishnavi Rindhe
Krutika Patil
Cancer is a highly heterogeneous disease, characterized by genetic, epigenetic, transcriptomic, proteomic, and metabolic variability not only between patients but also within the same tumor microenvironment [1]. This heterogeneity significantly complicates the design of standardized therapies. Traditional treatment modalities such as chemotherapy, radiotherapy, and even some targeted therapies often fail to achieve complete remission due to this diversity, contributing to therapy resistance and disease recurrence. The challenge of intratumor and interpatient variability is compounded by clonal evolution, which further diversifies the tumor cell population during treatment [2]. Subpopulations of cancer cells with distinct molecular profiles may respond differently, leading to partial or ineffective therapeutic outcomes [3]. Thus, a shift from a “one-size-fits-all” approach to a more tailored strategy is urgently needed. Personalized or precision oncology addresses this variability by tailoring treatment strategies to the unique molecular characteristics of an individual’s cancer. By utilizing omics technologies, including genomics, transcriptomics, and increasingly proteomics, it becomes possible to identify biomarkers that predict disease progression, drug response, and resistance [4]. In personalized cancer therapy, treatment decisions are based on the molecular signature of the tumor—such as mutations, protein expression levels, or post-translational modifications (PTMs)—rather than solely on histopathological classification. Clinical applications include selection of targeted drugs (e.g., EGFR inhibitors in lung cancer), immunotherapies guided by PD-L1 expression, or treatment stratification using companion diagnostics [5]. While genomics has been instrumental in uncovering mutations and alterations associated with cancer, proteomics provides complementary and functionally relevant insights by directly measuring the proteins, which are the final effectors of cellular function [6]. Proteomic profiling serves several critical functions in oncology. It helps identify tumor-specific biomarkers, track dynamic changes in the proteome in response to therapy, and detect post-translational modifications such as phosphorylation and acetylation, which are crucial for regulating signaling cascades [7]. Additionally, proteomics enables the stratification of patients for targeted therapies based on their unique protein expression profiles. Recent advancements in mass spectrometry-based proteomics and protein microarrays have made it possible to analyze large sets of proteins from tumor biopsies, plasma, and other biofluids. These technologies facilitate the mapping of intracellular signaling networks, the discovery of resistance mechanisms, and the identification of actionable targets in real-time clinical settings [8]. Furthermore, integrative multi-omics approaches—particularly proteogenomics—combine genomic and proteomic data to provide a more comprehensive understanding of tumor biology. Initiatives like the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have demonstrated how alterations in the proteome correlate with phenotypic heterogeneity and can influence treatment outcomes, reinforcing the role of proteomics in advancing precision medicine [9]. This review aims to provide a comprehensive overview of how proteomics is revolutionizing personalized cancer therapy. It begins by examining recent technological and analytical advancements in the field of cancer proteomics, shedding light on the innovations that are enhancing our ability to study proteins at scale and with greater specificity. The review then explores key clinical applications, including the discovery of biomarkers, the prediction of drug resistance, and the development of personalized therapeutic strategies tailored to individual patient profiles. In addition, it highlights the current challenges and outlines future directions for integrating proteomics into routine clinical oncology practice. By synthesizing the latest findings, this paper underscores the indispensable role of proteomics in fulfilling the promises of precision oncology and transforming cancer treatment paradigms. Figure No.1, illustrates how different sample types (tissue, serum, urine) feed into multi-omics analyses—including proteomics—to yield patient-specific insights that inform diagnostics, prognostics, and therapy selection
10.5281/zenodo.16810308